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The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.

Semiconductors are the center of the fourth industrial revolution as they are key components for all electronics. Exposed wafers made of silicon (Si), which can easily oxidize, convert to silicon dioxide (SiO₂). The surface of SiO₂ wafers consists of many Si-OH bonds, allowing them to easily bond with water, resulting in a “wet” or hydrophilic condition. We sought to determine a way to modify the surface of SiO₂ wafers to become hydrophobic to ensure safe wet cleaning.

Athletes with performance anxiety may struggle to play their best and enjoy the game. Various factors may impact how much anxiety an athlete feels, including how much experience they have in the sport. Concha-Ortiz and Navins survey teenage club volleyball players to look for relationships between years of experience and performance anxiety symptoms.

Here, the authors compared financial advice output by chat-GPT to actual Reddit comments from the "r/Financial Planning" subreddit. By assessing the model's response content, length, and advice they found that while artificial intelligence can deliver information, it failed in its delivery, clarity, and decisiveness.

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

Patients with cleft palate frequently struggle with speech issues such as nasal or congested speech. Lin and Parkinson conduct a meta-analysis to determine how two common types of cleft palate repair surgery compare in terms of their effects on patient's speech.

The authors survey how emotional responses to music differ across cultures and the impact of hearing loss on these emotional responses.

The authors looked at the effects of commonly used anesthetics in rodents on brain activity (specifically quasi-periodic patterns). Understanding effects on brain activity is important for researchers to understand when choosing rodent models for disease.

The study explored the role of testosterone beyond its well-established effects on male sex characteristics, focusing on its association with non-communicable diseases (NCDs) like obesity and type 2 diabetes (T2D), using Mendelian randomization (MR) analysis on genomic data.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.